基于混沌神经网络的分类算法

提出了一种基于混沌神经网络的分类算法,利用改进的进化策略对多个三层前馈混沌神经网络同时进行训练.训练好各个分类模型以后,将待识别数据分别输入,混沌神经网络分类模型输出最终分类结果.实验结果表明,该算法可以较好地进行数据分类,而且与传统的神经网络算法以及决策树算法相比,在分类精度和识别率方面均有一定的改善,体现出较好的稳定性.

Classification Algorithm Based on a Chaotic Neural Network

In this paper, a classification algorithm based on Chaotic Neural Network(CNN) was presented, which established classifiers by a group of threclayer feed-forward CNN. The chaotic neural networks were trained an improving algorithm. The class label of the identifying data could first be evaluated by each chaotic neural network, and the final classification result was obtained. Experimental results show that the algorithm CNN is effective for the classification,and has the better performance in classification precision, stability comparing with the traditional neural network algorithms and decision trees algorithm.